Advancements in object detection: From machine learning to deep learning paradigms

Abstract

The evolution of object detection from traditional machine learning approaches to advanced deep learning techniques marks a significant milestone in the field of computer vision. Initially, object detection relied on algorithms such as Support Vector Machines (SVMs) and decision trees, leveraging handcrafted features like Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) for classification and recognition tasks. However, these methods exhibited limitations in scalability and adaptability to complex environments. The breakthrough came with the adoption of Convolutional Neural Networks (CNNs), which transformed the landscape by automating feature extraction, thereby enhancing detection accuracy and efficiency. Subsequent innovations in network architectures, such as R-CNN, YOLO, and SSD, have continually refined object detection capabilities, optimizing both speed and precision. This paper examines the progression of object detection technologies, focusing on the impact of deep learning models and the optimization of network structures. It also delves into the quantitative analysis of model performance, highlighting the role of data augmentation and advanced training techniques in overcoming real-world detection challenges. Through this exploration, the paper aims to provide comprehensive insights into the current state and future directions of object detection techniques.